Machine Learning Engineer MMH260714-11

• Role Purpose
• The Machine Learning Engineer is responsible for designing, developing, deploying,
• and maintaining machine learning models and AI-driven solutions that solve complex
• business problems. The role involves working closely with data scientists, software
• engineers, and business stakeholders to transform data into scalable and productionready intelligent systems.

Requirements

• 5+ yrs of hands-on experience in developing, training, and deploying Machine

Learning models in production environments.

• Strong proficiency in Python and Machine Learning frameworks such as Scikitlearn, TensorFlow, PyTorch, or Keras.
• Experience in data preprocessing, feature engineering, model evaluation, and

optimization using structured and unstructured datasets.

• Exposure to cloud-based ML solutions (Azure, AWS, or GCP), MLOps practices,

CI/CD pipelines, and model monitoring.

Duties and Responsibilities

KEY ACCOUNTABILITIES/KRAs/KPIs

• Design, build, train, validate, and deploy machine learning models to address

business challenges.

• Collaborate with business stakeholders to understand requirements and translate

them into AI/ML solutions.

• Perform data collection, cleansing, preprocessing, and feature engineering

activities.

• Evaluate model performance using appropriate metrics and optimize models for

accuracy and scalability.

• Develop and maintain data pipelines to support machine learning workflows.
• Implement MLOps best practices for model deployment, version control,

monitoring, and retraining.

• Work closely with engineering teams to integrate ML solutions into production

systems.

• Conduct exploratory data analysis and derive meaningful insights from large

datasets.

• Prepare technical documentation, model reports, and implementation guides.
• Ensure adherence to security, governance, and compliance standards in AI

implementations.

• Support production issues and continuously improve deployed machine learning

solutions.

• Participate in sprint planning, code reviews, and technical discussions.
• Develop and optimize APIs and services for serving machine learning models in

production environments.

• Monitor model performance, drift, and data quality, ensuring timely retraining

and enhancements.

• Build scalable and reusable ML solutions that can be leveraged across multiple

business use cases.

• Conduct proof of concepts (POCs) and feasibility assessments for new AI/ML

initiatives.

As an applicant, please verify the legitimacy of this job advert on our company career page.

• 346021306